Designed and trained machine learning model to detect and classify distracted driver by means of convolution neural network

Implemented data pipeline to preprocess and dewarp 360 degree image frames from driving videos which is being used to train machine learning model for distracted driver detection

Taught programming (MIT course 6.0001) and machine learning (MIT course 6.S198) to students; Guided students on problem sets and various class projects associated with these courses

Worked with MIT professors and course staff to write problem sets and develop syllabus content for programming and machine learning courses in MIT

Worked on developing new tests on stability and performance of neural network to gain insight on their generalization and accuracy;

Implemented various convolutional and recurrent neural networks using frameworks like tensorflow and keras